The Inevitable Challenge of Ethical Dilemmas in Optometry, Part 2: Professional Relationships and Practices in the Spotlight
Bibliographic record
Abstract
Health care professionals sometimes have to choose between options that are each less than optimal, and thereby risk compromising an ethical principle. Despite the impact they can have on these professionals and the population they serve, ethical dilemmas have never been studied in optometry. Objective. This article is the second in a series of three reporting the results of a study that aimed to identify and describe the ethical dilemmas faced by optometrists. Method. A total of 240 optometrists completed an online questionnaire concerning ethical dilemmas encountered during their career. Results. A major source of ethical dilemmas for optometrists is conflicts with other optometrists, as well as with opticians and ophthalmologists. Other situations, such as being confronted with cases at the limit of one’s competency and the disclosure of personal information, are also important ethical issues. Conclusion. Optometrists experience ethical questioning that is likely to cause them stress and compromise the well-being of the public. The next and final article in this series will reveal ethical dilemmas concerning the optometrist/patient relationship and offer suggestions for optometrists to be better prepared for dealing with the various ethical issues related to the practice of their profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".